Microsoft Technology Licensing, LLC

University of Science and Technology Beijing

Beijing Sanotech Co., Ltd

Jiulong Wang

Zheng Chen

Jun Yan

Ning Liu

Xiao Bi Liang

Lei Ji

Jingwei Zhu

Shuyi Du

Liying Song

Hongqing Song

The present disclosure provides a method and device for predicting remaining oil distribution based on historical and predictive reservoir knowledge. The method includes: establishing complete data sets according to relevant historical dynamic and static monitor data of oil wells in a target reservoir; training and testing a long and short-term memory (LSTM) model using the complete data sets; predicting production of a single well at a preset moment by using the trained LSTM model; acquiring three-phase saturations of the single well at the preset moment based on the production predicting results and experience of reservoir experts; and according to the three-phase saturations of the single well, setting boundary conditions and physical constraints for the reservoir, and deriving an oil-gas-water three-phase saturation field at the preset moment using Kriging interpolation to obtain a prediction result of remaining oil distribution in the target reservoir.

Method and device for predicting remaining oil distribution based on historical and predictive reservoir knowledge

The related links recommendation technique described herein employs combined collaborative filtering to recommend related web pages to users. The technique creates multiple collaborative filters which are combined in order to create a combined collaborative filter to recommend web pages similar to a given web page to a user. One query-based collaborative filter is created by using query search clicks (e.g., user input device selection actions on search results returned in response to a search query). Another user-behavior-based collaborative filter is created by using query search clicks and user clicks while browsing websites (e.g., user input device selection actions while a user is browsing websites). Lastly, another content-based collaborative filter based on similar content of web pages is created by finding web pages with similar content.

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Beijing, China

Jiulong Wang